Fault Propagation Reasoning and Diagnosis for Computer Networks Using Cyclic Temporal Constraint Network Model

This paper reports the development of a software system, SHERLOCK, for fault diagnosis in power distribution ring networks. The system consists of a fault diagnosis subsystem implemented using Prolog and a user interface subsystem developed in the SmallTalk environment. This paper first gives an account of faults which may occur in distribution ring networks together with the fault symptoms and hidden defects. It then summarizes the general fault diagnosis rules. The methods of representation of network, faults, symptoms and defects are developed and the search procedure for searching the network busbars in the fault diagnosis process is outlined. This subsystem can be accessed by operation engineers through the user interface subsystem which displays in graphical form the network and the fault solutions generated. The usefulness of SHERLOCK is illustrated by its application to an 11kV 3-leg ring network.

Fault diagnosis is a critical activity in the comprehensive support for equipment operation. Relevant research about it is becoming more and more popular. Ontology-based fault diagnosis is an important method for diagnosing faults. Due to the expressive ability, knowledge sharing and knowledge reuse based on deep semantics, and supporting for logical reasoning, ontology-based fault diagnosis has received more and more attention from researchers in the past decade. The fault diagnosis ontology describes the core concepts involved in diagnosing faults and the relationship among the concepts. Its quality and usage determine the efficiency and effectiveness of fault diagnosis, thus has a great impact on fault diagnosis. However, its knowledge source and usage have not been analyzed comprehensively to give us a deep insight into this area yet. This paper investigates and studies the ontology-based fault diagnosis during the past decade, from the perspective of knowledge source and usage of fault diagnosis ontology. In summary, it can be seen that combining ontology with other methods improves the efficiency and the effect of fault diagnosis, and ontology-based fault diagnosis needs to be integrated into the whole process of fault diagnosis. To make knowledge of fault diagnosis complete, machine learning shall be used for the ontological engineering.

Time fault propagation graph (TFPG) is a directed graph model to describe the dynamic causality of the system. According to TFPG model, the system fault can be inferred from the observed events and the fault time can be estimated. In this paper, the process of fault diagnosis using time fault propagation graph is researched, and TFPG fault diagnosis algorithm framework is designed. In order to improve the effectiveness of fault diagnosis, the improved hypothesis initialization algorithm based on event and improved hypothesis update algorithm based on incremental reasoning are developed in the paper. Finally, we take Right Inboard Power Control Unit (PCU-RIB) as an example. According to the system characteristics and fault types, the TFPG model of PCU-RIB is constructed. Then the improved fault diagnosis algorithm based on TFPG is used for simulation verification. The results show that the improved algorithm has good robustness, can correctly eliminate the interference of false alarms and identify the corresponding fault.KeywordsFault diagnosisTime fault propagation diagramHealth managementTime consistency

An open-set fault diagnosis framework for MMCs based on optimized temporal convolutional network

With the development of big data, artificial intelligence and other technologies, data-driven aviation equipment fault diagnosis and prediction technology has gradually become a research hotspot in the aviation field. Many typical intelligent algorithm models have been applied to this field. However, limited by the airborne embedded computing environment, there are still some problems in the deployment of intelligent prediction models represented by deep neural networks on aircraft. This paper summarizes and analyzes the research and application of typical deep neural networks such as convolutional neural networks in the field of aircraft fault diagnosis and prediction. Facing the airborne embedded environment, the current difficulties in deploying the deep neural network algorithm model in the airborne environment are analyzed. The development direction of the application of fault prediction and diagnosis algorithms represented by neural networks in the future is discussed.

Dynamic model-based fault diagnosis for (bio)chemical batch processes

This work presents a framework for fault detection and diagnosis of multimode industrial processes. It is shown that the proposed method to generate features for the faults provides a generalization that can allow the fault diagnosis in modes for which no training was performed. The proposed method is applied to a sophisticated simulator of a continuous stirred tank reactor (CSTR). Experimental results suggest the usefulness of this approach for the detection of faults under operating conditions not considered during training phase.

This chapter discusses various issues and techniques related to machine and process condition monitoring and fault diagnosis. The use of expert systems and neural networks for condition monitoring and fault diagnosis is described.

The autonomous underwater vehicle (AUV) frequently operates in harsh underwater environments, and timely fault diagnosis of the AUV can prevent mission failure and equipment loss. Data‐driven methods based on a single data source have been widely utilized for fault diagnosis of the AUV because they do not require the construction of complex mechanism models and have high fault diagnosis accuracy. However, these methods face challenges in accomplishing complex fault diagnosis tasks because the single data source provides very restricted fault features. To address this issue, an attention mechanism‐based multisensor data fusion neural network (MDFNN) for AUV fault diagnosis is proposed in this work. First, a feature extraction layer based on the two‐dimensional (2D) convolutional method with a 1D kernel is introduced to extract features from each sensor data separately, significantly optimizing the model architecture. Second, an efficient channel attention mechanism‐based feature fusion layer is proposed to reassign weights to the features of each sensor data, enabling the model to focus more on crucial features. Finally, the fused features are input to the fully connected layers and softmax layer to realize the fault diagnosis of multisensor data. In the end, the diagnostic performance of the proposed MDFNN is evaluated utilizing real AUV experimental data. The experiment shows that the proposed MDFNN has a very fast convergence speed and 98.37% fault diagnosis accuracy, demonstrating its excellent fault diagnosis performance. The proposed MDFNN provides a generalized and simply structured fault diagnosis framework for the AUV with multiple types of sensor data, providing significant engineering value.

Residual attention temporal recurrent network for fault diagnosis of gearboxes under limited labeled data

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Efficient condition monitoring and fault diagnosis is an essential task to ensure the generation performance and reliability of photovoltaic (PV) systems. This paper proposes an online algorithm to diagnose faults of PV module based on multi-class support vector machine (M-SVM). The simulation models of the photovoltaic module are implemented and the output power generation characteristics of PV modules under two typical fault conditions (line-to-line fault and abnormal degradation fault) are analyzed. Through the combination of mathematical model and simulation experiments of PV modules, the fill factor (FF) and the fault type factor K are introduced as feature parameters in the fault diagnosis process. In addition, to address the nonlinear problem in fault diagnosis and improve the single support vector machine, a fault diagnosis method based on the multi-class classification method of one-against-one (OAO) algorithm is proposed. The proposed solution is implemented and simulated through experiments and the numerical result clearly demonstrates its effectiveness and diagnosis accuracy.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

A novel fault diagnosis method for turbo-generator set based on fractal exponent theory and wavelet network is presented. When faults occur, they usually produce nonstationary vibration signals. The wavelet transform is used to localizes the characteristics of vibration signal in the time frequency domains and in a view of the inter relationship of wavelet transform between fractal theory, the whole and local fractal exponents obtained from wavelet transform coefficients as features are presented for extracting fault signals, which are inputted into radial basis function for fault pattern recognition. The improved Levenberg-Marquardt (LM) optimization technique is used to complete the network structure parameters. By means of choosing enough samples to train the fault diagnosis network and the information representing the faults is input into the trained wavelet network, and according to the output result the type of fault can be determined. The practical diagnosis for stator temperature fluctuation and rotor vibration demonstrates that the wavelet fractal network can provide an effective way to diagnosis faults for turbo-generator set in power system.